Review Article
SERS-based approaches in the investigation of bacterial metabolism, antibiotic resistance, and species identificationAuthor links open overlay panel,
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- Key Laboratory of Emergency and Trauma, Ministry of Education, Key Laboratory of Hainan Trauma and Disaster Rescue, Key Laboratory of Haikou Trauma, The First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou 571199, China
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- Engineering Research Center for Hainan Bio-Smart Materials and Bio-Medical Devices, Key Laboratory of Hainan Functional Materials and Molecular Imaging, College of Emergency and Trauma, Hainan Medical University, Haikou 571199, China
Volume 336, 5 August 2025, 126051
https://doi.org/10.1016/j.saa.2025.126051
Highlights
•SERS enables sensitive detection of bacterial metabolism and antibiotic resistance.
•Integration of SERS and machine learning enables precise bacterial identification.
•SERS advances clinical diagnostics via photothermal therapy and portable devices.
Abstract
Surface-enhanced Raman scattering (SERS) is an inelastic scattering phenomenon that occurs when photons interact with substances, providing detailed molecular structure information. It exhibits various advantages including high sensitivity, specificity, and multiple-detection capabilities, which make it particularly effective in bacterial detection and antibiotic resistance research. In this review, we review the recent development of SERS-based approaches in the investigation of bacterial metabolism, antibiotic resistance, and species identification. Although the promising applications have been realized in clinical microbiology and diagnostics, several challenges still limit the further development, including signal variability, the complexity of spectral data interpretation, and the lack of standardized protocols. To overcome these obstacles, more reproducible and standardized methodologies, particularly in nanomaterial design and experimental condition optimization. Furthermore, the integration of SERS with machine learning and artificial intelligence can automate spectral analysis, improving the efficiency and accuracy of bacterial species identification, resistance marker detection, and metabolic monitoring. Combining SERS with other analytical techniques, such as mass spectrometry, fluorescence microscopy, or genomic sequencing, could provide a more comprehensive understanding of bacterial physiology and resistance mechanisms. As SERS technology advances, its applications are expected to extend beyond traditional microbiology to areas like environmental monitoring, food safety, and personalized medicine. In particular, the potential for SERS to be integrated into point-of-care diagnostic devices offers significant promise for enhancing diagnostics in resource-limited settings, providing cost-effective, rapid, and accessible solutions for bacterial infection and resistance detection.
Graphical abstract
Keywords
Surface-enhanced Raman scattering (SERS)
Bacterial metabolism
Antibiotic resistance
Species identification
Disease diagnosis
1. Introduction
Bacterial metabolism plays important roles in the survival and pathogenicity of microorganisms [
1]. Metabolic processes are highly dynamic, which can be influenced by environmental factors including nutrient availability, temperature, pH, and the presence of antimicrobial agents [
2]. The detection of bacterial metabolism can provide critical information about the physiological state of bacteria and their response to external stimuli. Antibiotic resistance has become one of the most pressing challenges that threatening the global health and current antimicrobial treatment, which makes the treatment of infections more difficult and complicated [
3,
[4],
[5],
[6],
[7]]. Accurate and rapid bacterial species identification is essential for the effective treatment of infections, particularly in cases of polymicrobial infections where multiple species are present [
8]. Traditional bacterial identification methods, such as culture and biochemical testing, can take several days to yield results, during which time the infection may progress. Furthermore, these methods are not always capable of distinguishing between closely related bacterial species, which can lead to misidentification and inappropriate treatment [
9,
10].
In recent decades, the methods to rapidly and accurately identify bacterial species, investigate their metabolic processes, and determine their resistance to antibiotics have attracted increasing attentions in both clinical diagnostics and public health management [
11,
12]. Although the traditional methods including culture-based techniques, biochemical tests, and polymerase chain reaction (PCR) assays have been widely used, their inherent limitations still limit their in situ and non-invasive detection, such as lengthy processing times, labor-intensive procedures, and a reliance on specialized equipment and skilled personnel [
13,
14]. Moreover, many of these methods lack the sensitivity and specificity needed for the rapid identification of bacterial species, especially in polymicrobial infections [
15]. Therefore, there is an urgent need to develop novel analytical tools capable of overcoming these barriers. Various detection techniques have been developed including fluorescence, colorimetric, magnetic resonance imaging (MRI) and so on [
16,
17]. Among these, surface-enhanced Raman scattering (SERS) has emerged as a promising and powerful technique, offering unique advantages in the fields of bacterial diagnostics, metabolic studies, and antimicrobial resistance monitoring [
18].
SERS is an advanced vibrational spectroscopic technique that enhances the Raman scattering signal of molecules adsorbed onto rough metal surfaces or nanostructured materials. The enhancement occurs due to the interaction between the incident light and the surface plasmon resonance of the metal nanoparticles, which leads to a significant increase in the intensity of the Raman scattering. SERS allows for the detection of molecular vibrations at the nanometer scale, providing detailed information on the chemical composition and molecular structure of samples [
19,
20]. In the context of bacterial analysis, SERS can be used to interrogate bacterial cells without the need for complex sample preparation, making it an ideal tool for real-time, in situ analysis. This capability is particularly useful for the study of bacterial metabolism, resistance mechanisms, and species identification, where rapid and reliable results are paramount [
21].
In this review, we provide a retrospective of the application of SERS method toward detection of Gram-positive and Gram-negative bacterial metabolism, antibiotic resistance, and species identification. Label-free and label-based SERS detection with the use of Raman reporters have been summarized here. Then, the discussion is focused on the recent development based on SERS detection of metabolic process, antibiotic resistance, and species identification. We also extend SERS to the applications of Bactericidal treatment, such as photothermal therapy (PTT) and photodynamic therapy (PDT). Finally, we summarize this review and put forward outlooks for the limitations that urgently need to be addressed. In addition, the future development of SERS methods has also been demonstrated. This review is expected to provide valuable insights for future investigations.
3. Summary and outlook
SERS has gained increasing attention as a transformative tool in microbiological research, offering unprecedented sensitivity, specificity, and non-invasive capabilities for the study of bacterial metabolism, antibiotic resistance, and species identification.
SERS provides a unique platform for investigating bacterial metabolism to monitor metabolic shifts in response to environmental changes, stress factors, and therapeutic interventions due to the capabilities of non-invasiveness, high sensitivity with multiplex detection. Unlike conventional techniques, SERS realizes the successful detection of subtle changes in the molecular profiles of bacterial cells, which has been demonstrated in the study of bacterial responses to antibiotics, the identification of biomarkers related to stress or pathogenicity, and the exploration of bacterial growth patterns. In addition, SERS-based metabolic profiling offers a valuable approach for real-time monitoring of bacterial activity, greatly improving our comprehension of microbial physiology and facilitating the identification of potential novel therapeutic targets. In the context of antibiotic resistance, SERS exhibits great potential in detecting resistance markers in bacterial cells exposed to antimicrobial agents. By recognizing unique molecular patterns linked to resistance mechanisms such as changes in cell wall structure, increased efflux pump activity, or alterations in metabolic pathways-SERS provides a fast alternative to traditional culture-based resistance testing. The real-time detection of resistance without prolonged culturing provides the significant potential for the application in diagnostic process. On the one hand, SERS exhibits excellent sensitivity and specificity to distinguish the different bacterial species through analyzing the unique spectral fingerprints of bacteria, which efficiently saves the time and provides the rapid, reliable method for clinical samples compared to the conventional methods. On the other hand, SERS method also can detect the difficult-to-culture or unculturable species, which are often missed by traditional diagnostic techniques. In addition, the integration of machine learning algorithms with SERS further enhances its ability to identify bacterial species and differentiate between closely related strains.
Although SERS-based approaches have made significant progress, several obstacles including the variability of SERS signals, the complexity of spectral data interpretation, and the need for standardized protocols still limit the further applications in clinical microbiology and diagnostics. Due to the inherent variability of SERS signals, the sensitivity and repeatability of Raman spectra are highly dependent on the choice of substrate, the preparation of bacterial samples, and the experimental conditions. The variability can lead to inconsistent results and hinder the reproducibility of SERS-based experiments. To overcome this challenge, more uniform and stable SERS substrates have been developed for improving the reliability and consistency of Raman signals. The interpretation of the complex Raman signals from the matrix background often interferes the effective results. However, extracting meaningful insights from these data requires advanced analytical techniques. The integration of machine learning and artificial intelligence (AI) with SERS opens a new era to overcome this obstacle. By leveraging algorithms trained on large datasets of bacterial spectra, AI can help automate the process of spectral analysis, allowing for faster and more accurate identification of bacterial species, detection of resistance markers, and monitoring of metabolic changes. However, further refinement of these algorithms, particularly in terms of handling large-scale datasets and improving model accuracy, is needed for widespread clinical implementation. Furthermore, the potential for SERS to be incorporated into point-of-care diagnostic devices is another area of interest. Portable, easy-to-use SERS-based devices could revolutionize clinical diagnostics, enabling rapid, on-site detection of bacterial infections and antibiotic resistance in resource-limited settings. Such devices would provide a cost-effective alternative to traditional microbiological techniques, making high-quality diagnostics more accessible worldwide.
In conclusion, SERS holds great promise as a tool for advancing our understanding of bacterial metabolism, antibiotic resistance, and species identification, which exhibits high sensitivity, rapid analysis, and the ability to provide fingerprint information in both clinical diagnostics and microbiological research. In the future, SERS combined with artificial intelligence (AI) will enable faster and more accurate bacterial species identification, antibiotic resistance marker detection and metabolic changes monitoring. Portable, small-scale SERS diagnostic equipment may revolutionize the approach to clinical diagnosis, and SERS combined with other analytical techniques can also provide a more comprehensive understanding of bacterial behavior and drug response. As these technologies evolve, SERS is poised to become an indispensable tool in the fight against bacterial infections and antimicrobial resistance, offering a powerful means to improve patient outcomes and public health worldwide.
